Calibrated Uncertainty in Retrieval-Augmented Generation
Retrieval-augmented generation grounds answers in retrieved documents, yet models remain over-confident when the retrieved evidence is irrelevant or contradictory. We propose a lightweight confidence estimator that combines the generator's token probabilities with a retrieval-agreement score, and calibrate it with temperature scaling on a small validation set. On three question-answering datasets the estimator lowers expected calibration error from 0.19 to 0.06 and enables selective answering: abstaining on the 20% least confident questions raises accuracy from 71% to 84%. The method needs no model retraining.